A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

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Abstract Fault section identification and determining its location are important aspects to reduce down/repair time, speed up restoration of power supply and to improve the reliability. In this paper combined wavelet and artificial neural network based directional protection scheme is proposed for double circuit transmission lines using single end data to identify the faulty section and its location with reach setting up to 99% of line length. The proposed method requires the three phase currents and voltage to be measured at one end of the double circuit transmission line modelled using distributed parameter line model which also considers the effect of shunt capacitance. Approximate coefficients feature vector of the three phase voltage and current are extracted using discrete wavelet transform to train the ANN with Levenberg Marquardt algorithm. The proposed scheme involves two stages. The first stage identifies the zone/section of the fault and the second stage calculates the fault location from the relaying point. The proposed combined Wavelet and ANN based fault location scheme is also compared with ANFIS based fault location scheme. The test results of the proposed scheme show that the fault section identification and location estimation is very accurate and the average percentage error in fault location estimation is within 0.001%. This method is adaptive to the variation of fault type (both forward and reverse), fault inception angle, fault location and fault resistance. The main advantage of the proposed scheme is that it offers primary protection to 99% of line length using single end data only and also backup protection to the adjacent forward and reverse line sections.